{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantile regression\n",
    "\n",
    "### Setup\n",
    "\n",
    "We first load some modules and retrieve the data. Conveniently, the Engel dataset is shipped with statsmodels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from __future__ import print_function\n",
    "import patsy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.regression.quantile_regression import QuantReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>foodexp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>420.157651</td>\n",
       "      <td>255.839425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>541.411707</td>\n",
       "      <td>310.958667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>901.157457</td>\n",
       "      <td>485.680014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>639.080229</td>\n",
       "      <td>402.997356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750.875606</td>\n",
       "      <td>495.560775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>945.798931</td>\n",
       "      <td>633.797815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>829.397887</td>\n",
       "      <td>630.756568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>979.164836</td>\n",
       "      <td>700.440904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1309.878940</td>\n",
       "      <td>830.958622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1492.398744</td>\n",
       "      <td>815.360217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        income     foodexp\n",
       "0   420.157651  255.839425\n",
       "1   541.411707  310.958667\n",
       "2   901.157457  485.680014\n",
       "3   639.080229  402.997356\n",
       "4   750.875606  495.560775\n",
       "5   945.798931  633.797815\n",
       "6   829.397887  630.756568\n",
       "7   979.164836  700.440904\n",
       "8  1309.878940  830.958622\n",
       "9  1492.398744  815.360217"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sm.datasets.engel.load_pandas().data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Least Absolute Deviation\n",
    "\n",
    "The LAD model is a special case of quantile regression where q=0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         QuantReg Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                foodexp   Pseudo R-squared:               0.6206\n",
      "Model:                       QuantReg   Bandwidth:                       64.51\n",
      "Method:                 Least Squares   Sparsity:                        209.3\n",
      "Date:                Tue, 28 Aug 2018   No. Observations:                  235\n",
      "Time:                        15:58:41   Df Residuals:                      233\n",
      "                                        Df Model:                            1\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     81.4823     14.634      5.568      0.000      52.649     110.315\n",
      "income         0.5602      0.013     42.516      0.000       0.534       0.586\n",
      "==============================================================================\n",
      "\n",
      "The condition number is large, 2.38e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    }
   ],
   "source": [
    "mod = smf.quantreg('foodexp ~ income', data)\n",
    "res = mod.fit(q=.5)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the results\n",
    "\n",
    "We estimate the quantile regression model for many quantiles between .05 and .95, and compare best fit line from each of these models to Ordinary Least Squares results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for plotting\n",
    "\n",
    "For convenience, we place the quantile regression results in a Pandas DataFrame, and the OLS results in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      q           a         b        lb        ub\n",
      "0  0.05  124.880098  0.343361  0.268632  0.418090\n",
      "1  0.15  111.693660  0.423708  0.382780  0.464636\n",
      "2  0.25   95.483539  0.474103  0.439900  0.508306\n",
      "3  0.35  105.841294  0.488901  0.457759  0.520043\n",
      "4  0.45   81.083647  0.552428  0.525021  0.579835\n",
      "5  0.55   89.661370  0.565601  0.540955  0.590247\n",
      "6  0.65   74.033435  0.604576  0.582169  0.626982\n",
      "7  0.75   62.396584  0.644014  0.622411  0.665617\n",
      "8  0.85   52.272216  0.677603  0.657383  0.697823\n",
      "9  0.95   64.103964  0.709069  0.687831  0.730306\n",
      "{'a': 147.47538852370573, 'b': 0.48517842367692354, 'lb': 0.4568738130184233, 'ub': 0.5134830343354237}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    }
   ],
   "source": [
    "quantiles = np.arange(.05, .96, .1)\n",
    "\n",
    "def fit_model(q):\n",
    "    res = mod.fit(q=q)\n",
    "    return [q, res.params['Intercept'], res.params['income']] + res.conf_int().loc['income'].tolist()\n",
    "\n",
    "models = [fit_model(x) for x in quantiles]\n",
    "models = pd.DataFrame(models, columns = ['q', 'a', 'b', 'lb', 'ub'])\n",
    "ols = smf.ols('foodexp ~ income', data).fit()\n",
    "ols_ci = ols.conf_int().loc['income'].tolist()\n",
    "ols = dict(a = ols.params['Intercept'], \n",
    "          b = ols.params['income'],\n",
    "          lb = ols_ci[0], \n",
    "          ub = ols_ci[1])\n",
    "print(models)\n",
    "print(ols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First plot\n",
    "\n",
    "This plot compares best fit lines for 10 quantile regression models to the least squares fit. As Koenker and Hallock (2001) point out, we see that:\n",
    "\n",
    "1). Food expenditure increases with income.\n",
    "\n",
    "2). The dispersion of food expenditure increases with income.\n",
    "\n",
    "3). The least squares estimates fit low income observations quite poorly (i.e. the OLS line passes over most low income households)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x246067852b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(data.income.min(), data.income.max(), 50)\n",
    "get_y = lambda a, b: a + b * x\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "for i in range(models.shape[0]):\n",
    "    y = get_y(models.a[i], models.b[i])\n",
    "    ax.plot(x, y, linestyle = 'dotted', color = 'grey')\n",
    "y = get_y(ols['a'], ols['b'])\n",
    "\n",
    "ax.plot(x, y, color='red', label='OLS')\n",
    "ax.scatter(data.income, data.foodexp, alpha=.2)\n",
    "ax.set_xlim((240, 3000))\n",
    "ax.set_ylim((240, 2000))\n",
    "legend = ax.legend()\n",
    "ax.set_xlabel('Income', fontsize = 16)\n",
    "ax.set_ylabel('Food Expenditure', fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second plot\n",
    "\n",
    "The dotted black lines form 95% point-wise confidence band around 10 quantile regression estimates (solid black line). The red lines represent OLS regression results along with their 95% confindence interval.\n",
    "\n",
    "In most cases, the quantile regression point estimates lie outside the OLS confidence interval, which suggests that the effect of income on food expenditure may not be constant across the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2460664a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = models.shape[0]\n",
    "p1 = plt.plot(models.q, models.b, color='black', label='Quantile Reg.')\n",
    "p2 = plt.plot(models.q, models.ub, color='black', linestyle='dotted')\n",
    "p3 = plt.plot(models.q, models.lb, color='black', linestyle='dotted')\n",
    "p4 = plt.plot(models.q, [ols['b']] * n, color='red', label='OLS')\n",
    "P5 = plt.plot(models.q, [ols['lb']] * n, color='red', linestyle='dotted')\n",
    "p6 = plt.plot(models.q, [ols['ub']] * n, color='red', linestyle='dotted')\n",
    "plt.ylabel(r'$\\beta_{income}$')\n",
    "plt.xlabel('Quantiles of the conditional food expenditure distribution')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
